2007 paul vanraden, curt van tassell, george wiggans, tad sonstegard, and jeff o’connell animal...

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200 7 Paul VanRaden, Curt Van Tassell, George Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff Wiggans, Tad Sonstegard, and Jeff O’Connell O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics Laboratory, USDA Agricultural Research Service, Beltsville, MD, USA 200 8 Genomic Prediction Genomic Prediction Results Results

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Page 1: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

2007

Paul VanRaden, Curt Van Tassell, George Wiggans, Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’ConnellTad Sonstegard, and Jeff O’Connell

Animal Improvement Programs Laboratory and Bovine Functional Genomics Laboratory, USDAAgricultural Research Service, Beltsville, MD, [email protected]

2008

Genomic Prediction ResultsGenomic Prediction Results

Page 2: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Measuring Genetic SimilarityMeasuring Genetic Similarity

Cattle genome sequenced in 2004• 30 chromosome pairs (including X,Y)• 3 billion letters from each parent

Illumina Bovine SNP50TM Chip• 58,000 genetic markers in 2007• 39,835 used in genomic predictions• Cost about $200 per animal

Page 3: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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How Related are Relatives?How Related are Relatives?

Example: Full sibs • are expected to share 50% of their

DNA on average, with SD of 5% • may actually share 40% to 60% of

their DNA because each inherits a different mixture of chromosome segments from the two parents.

• SD 3.5% reported previously was low

Page 4: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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SimulatedSimulated Results (Apr 2007) Results (Apr 2007)

1777 older and 500 younger bulls

10,000 SNPs and 100 QTLs

Reliability vs parent average REL • 58% vs 36% for young bulls• Higher REL if major loci and

Bayesian methods used, lower if many loci (>100) affect trait

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SimulatedSimulated Results (2008) Results (2008)

8271 older and 1984 younger bulls

40,000 SNPs and 500 QTLs

Provided timing, memory test

Reliability vs parent average REL • 79% vs 37% expected for young bulls• 76% vs 37% observed in simulation

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GenotypedGenotyped Bulls (Feb 2007) Bulls (Feb 2007)from Cooperative Dairy DNA Repositoryfrom Cooperative Dairy DNA Repository

DNA of bulls stored in Beltsville (BFGL)

2560 proven bulls used to computed predictions• Bulls born 1994-1996 with >75% reliability

of Net Merit• Plus ancestor bulls born 1952-1993

659 later bulls used to test predictions• Born 2001 with >75% reliability of Net Merit

Page 7: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Proposed Genotyping (Apr 2007)Proposed Genotyping (Apr 2007)

0

400

800

1200

1990 1994 1998 2002 2006

Nu

mb

er o

f B

ulls

ancestors

proven

predicted

calves

Data cutoff

Page 8: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Current Genotyped Animals (n=6005)Current Genotyped Animals (n=6005)

0

200

400

600

800

100019

50

1970

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

Year of Birth

Nu

mb

er o

f A

nim

als

Predictor

Predictee

Young

Page 9: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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AcknowledgmentsAcknowledgments

Funding: • NRI grants 2006-35205-16888, 16701• CDDR Contributors (NAAB, Semex)

Genotyping and DNA extraction:• BFGL, U. Missouri, U. Alberta,

GeneSeek, GIFV, and Illumina

Computing from AIPL staff

Page 10: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Genomic MethodsGenomic Methods

Direct genomic evaluation• Inversion for linear prediction, REL• Iteration for nonlinear prediction

Combined genomic evaluation• Traditional PA or PTA, subset PA or

PTA, and direct genomic combined by REL in 3 x 3 selection index

• Nonlinear genomic predictions used

Page 11: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Nonlinear and Linear Regressions Nonlinear and Linear Regressions for marker allele effectsfor marker allele effects

Page 12: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Actual Results (Feb 2007 data)Actual Results (Feb 2007 data)

August 2003 PTAs for 2650 older bulls to predict January 2008 daughter deviations for 569 younger bulls (total = 3119 bulls)

Results computed for 27 traits: 5 yield, 5 health, 16 conformation, and Net Merit

Nonlinear A used, B didn’t work

Page 13: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Marker P-Values for Net MeritMarker P-Values for Net Merit

Page 14: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Marker Effects for Net MeritMarker Effects for Net Merit

Page 15: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Marker Effects for MilkMarker Effects for Milk

Page 16: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Marker Effects for Final ScoreMarker Effects for Final Score

Page 17: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Reliabilities and R-square values comparing Reliabilities and R-square values comparing traditional to genomic predictionstraditional to genomic predictions

Squared corr (x100)

Reliability

Traditional Genomic Genomic

Trait PA Genomic PA Realized Gain

Net Merit 8 21 36 54 18

Milk 30 44 38 54 16

Fat 15 39 38 65 27

Protein 31 43 38 51 13

Fat % 28 58 38 72 34

Protein % 32 51 38 66 28

Page 18: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Reliabilities and R-square values comparing Reliabilities and R-square values comparing traditional to genomic predictionstraditional to genomic predictions

Squared corr (x100)

Reliability

Traditional Genomic Genomic

Trait PA Genomic PA Realized Gain

Prod Life 9 19 28 47 19SCS 7 23 32 54 22DPR 15 23 25 40 15SCE 19 23 31 36 5DCE 24 27 31 36 5Final Score 24 32 28 37 9

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Expected vs Observed ReliabilityExpected vs Observed Reliability

Reliability for predictee bulls • Average across traits: 57% expected

vs. 48% observed vs. 30% PA• Observed range 72% (fat pct) to 36%• PTA regressions .8 to .9 of expected

Redo 2003 cutoff using April data

Develop REL and PTA adjustments

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Clones and Identical TwinsClones and Identical Twins21HO2121, 21HO2125, 21HO2100, CAN6139300, CAN613930321HO2121, 21HO2125, 21HO2100, CAN6139300, CAN6139303

Traditional Genomic

Bull Dtrs NM$ REL NM$ REL

Triton - ETN 98 -363 82 -371 91

Triad - ETN 26 -306 68 -370 91

Trey - ETN 108 -395 83 -371 91

Loyalty 108 -185 78 -196 87

Lauriet 83 -203 76 -196 87

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X, X, YY, , Pseudo-autosomalPseudo-autosomal SNPs SNPs

487 SNPs

35 SNPs

0 SNPs

35 SNPs

Page 22: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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SNPs on X ChromosomeSNPs on X Chromosome

Each animal has two evaluations• Expected genetic merit of daughters• Expected genetic merit of sons• Difference is sum of effects on X• SD = .1 σG, smaller than expected

Correlation with sire’s daughter vs. son PTA difference was significant (P<.0001), regression close to 1.0

Page 23: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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SNP Density ComparisonSNP Density Comparison2130 older and 261 younger bulls2130 older and 261 younger bulls

REL of PA

Genomic REL

Trait 10K 20K 40K

Net Merit 35 45 49 48

Milk 37 49 52 51

Fat 37 52 55 57

Protein 37 52 54 53

Productive Life 28 44 45 42

SCS 31 44 45 47

Dtr Preg Rate 21 44 49 46

Page 24: 2007 Paul VanRaden, Curt Van Tassell, George Wiggans, Tad Sonstegard, and Jeff O’Connell Animal Improvement Programs Laboratory and Bovine Functional Genomics

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Genetic Evaluation AdvancesGenetic Evaluation Advancesand increases in genetic progressand increases in genetic progress

Year Advance % Gain

1935 Daughter-dam comparison 100

1962 Herdmate comparison 50

1974 Modified cont. comparison 5

1977 Protein evaluated 4

1973 Records in progress 10

1989 Animal model 4

1994 Net merit, PL, and SCS 50

2008 Genomic selection >40

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ConclusionsConclusions

Genomic predictions significantly better than parent average (P < .0001) for all 26 traits tested

Gains in reliability from 2650 bulls (Feb data) equivalent on average to 9 daughters with records

April data included 5285 proven bulls, more analysis needed